Using mixed methods research to develop an emergency department-based youth violence secondary intervention
Bibliographic record
Abstract
Violence is a significant cause of morbidity and mortality among youth worldwide. Using mixed methods is essential to understanding the complexity of addressing youth violence. Emergency Department (ED)-based interventions that target a high risk group are ideal because victims of violence are more likely to become repeat victims of violence. We used mixed methods research to develop an ED-based youth violence secondary intervention in Toronto, Canada. To determine where best to link the patient and program, we conducted quantitative studies using the population-based National Ambulatory Care Reporting System. Our first study demonstrated that focusing on admitted patients would miss most opportunities for intervention. Our second study examined the type of ED that would be most appropriate. While most efforts at secondary violence prevention target patients cared for at designated trauma centres, our work suggests that opportunities are greater outside these centres. We then performed a systematic review of ED-based youth violence secondary prevention programs. Finally, to ensure the best design of the intervention and to build important partnerships, we engaged in community-based participatory research with over 100 youth, parents and youth violence community workers. Using concept mapping we worked with community partners to develop a program that will link youth who visit the ED with injuries due to violence with community youth violence interventions. We will discuss the results of the above studies and share preliminary results from the pilot project of the ED-based youth violence intervention planned for the summer of 2010.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.209 | 0.121 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".